Rethinking career development for the AI era: a framework for training professionals in hybrid human–machine workplaces

Purpose This study aims to formulate a practical framework for human resource development (HRD) professionals supporting career development in artificial intelligence (AI)-augmented workplaces. As AI becomes embedded in career processes, from resume screening to performance evaluation, traditional career development approaches prove inadequate for preparing workers to navigate human–machine collaboration. Design/methodology/approach A narrative literature review synthesizes insights from career development theory, AI implementation research and posthumanist perspectives on human–technology interaction. The review strategically examines works that illuminate how careers function when humans and machines collaborate, focusing on practical implications for HRD practitioners rather than comprehensive coverage. Findings Three critical dimensions emerge that training professionals must address: developing hybrid skill capital, the ability to orchestrate effective partnerships between human judgement and AI systems, such as knowing when to override algorithmic recommendations; managing recursive career cycles where professionals experience repeated waves of reskilling (e.g. marketing professionals adapting first to programmatic advertising, then AI content generation, then predictive analytics) rather than linear progression; and supporting identity formation in algorithm-mediated environments where career paths emerge from interactions between personal aspirations and AI-driven recommendations for roles, learning paths and promotions. Taken together, these dimensions offer a working model for understanding and intervening in AI-augmented career development. Research limitations/implications The framework draws primarily from Western organizational contexts and focuses on professional workers, potentially limiting applicability across different cultural settings and job types. Future research should test interventions based on the model and explore its relevance in diverse contexts. Practical implications The framework provides concrete guidance for redesigning career development programs, including creating experimentation spaces for human–AI collaboration, proposing new mentoring models that accommodate multidirectional knowledge flows and implementing assessment approaches that measure collaboration effectiveness (how well individuals orchestrate human–AI partnerships), adaptation velocity (speed of integrating new AI capabilities) and identity coherence (maintaining purposeful career narratives while acknowledging algorithmic influence). Organizations can use these insights to prepare workers for careers increasingly shaped by algorithmic mediation. Originality/value This study develops a framework specifically designed for HRD professionals navigating AI integration in career development, one that differs from prior approaches in treating AI as constitutive rather than instrumental. Unlike previous work that treats AI as merely another tool, the model recognizes careers as emerging from distributed human–AI networks, providing both theoretical innovation and practical guidance for this transformation.